Traffic flow prediction using long short-term memory-Komodo Mlipir algorithm: metaheuristic optimization to multi-target vehicle detection
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About This Presentation
Multi-target vehicle detection in urban traffic faces challenges such as poor lighting, small object sizes, and diverse vehicle types, impacting traffic flow prediction accuracy. This study introduces an optimized long short-term memory (LSTM) model using the Komodo Mlipir algorithm (KMA) to enhance...
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IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 14, No. 4, August 2025, pp. 3343~3353
ISSN: 2252-8938, DOI: 10.11591/ijai.v14.i4.pp3343-3353 3343
Journal homepage: http://ijai.iaescore.com
Traffic flow prediction using long short-term memory-Komodo
Mlipir algorithm: metaheuristic optimization to multi-target
vehicle detection
Imam Ahmad Ashari
1,4
, Wahyul Amien Syafei
2
, Adi Wibowo
3
1
Doctoral Program of Information Systems, Universitas Diponegoro, Semarang, Indonesia
2
Faculty of Engineering, Universitas Diponegoro, Semarang, Indonesia
3
Faculty of Science and Mathematics, Universitas Diponegoro, Semarang, Indonesia
4
Faculty of Science and Technology, Universitas Harapan Bangsa, Purwokerto, Indonesia
Article Info ABSTRACT
Article history:
Received Dec 12, 2024
Revised Jun 24, 2025
Accepted Jul 13, 2025
Multi-target vehicle detection in urban traffic faces challenges such as poor
lighting, small object sizes, and diverse vehicle types, impacting traffic flow
prediction accuracy. This study introduces an optimized long short-term
memory (LSTM) model using the Komodo Mlipir algorithm (KMA) to
enhance prediction accuracy. Traffic video data are processed with YOLO
for vehicle classification and object counting. The LSTM model, trained to
capture traffic patterns, employs parameters optimized by KMA, including
learning rate, neuron count, and epochs. KMA integrates mutation and
crossover strategies to enable adaptive selection in global and local searches.
The model's performance was evaluated on an urban traffic dataset with
uniform configurations for population size and key LSTM parameters,
ensuring consistent evaluation. Results showed LSTM-KMA achieved a root
mean square error (RMSE) of 14.5319, outperforming LSTM (16.6827),
LSTM-improved dung beetle optimization (IDBO) (15.0946), and LSTM-
particle swarm optimization (PSO) (15.0368). Its mean absolute error
(MAE), at 8.7041, also surpassed LSTM (9.9903), LSTM-IDBO (9.0328),
and LSTM-PSO (9.0015). LSTM-KMA effectively tackles multi-target
detection challenges, improving prediction accuracy and transportation
system efficiency. This reliable solution supports real-time urban traffic
management, addressing the demands of dynamic urban environments.
Keywords:
Komodo Mlipir algorithm
Long short-term memory
Metaheuristic optimization
Multi-target vehicle detection
Traffic flow prediction
This is an open access article under the CC BY-SA license.
Corresponding Author:
Imam Ahmad Ashari
Doctoral Program of Information Systems, Universitas Diponegoro
Semarang, Indonesia
Email: [email protected]
1. INTRODUCTION
With advancements in communication technology and computer science, intelligent transportation
systems (ITS) have assumed an increasingly significant role in daily life [1]. Smart transportation has become
a cornerstone in the development of technology-based ITS to meet the evolving needs of urban societies [2].
It refers to an approach that integrates modern technology into transportation systems to enhance urban
mobility efficiency [3]. In the context of smart cities, cutting-edge technologies such as the internet of things
(IoT), data analytics, and artificial intelligence (AI) serve as foundational pillars for creating intelligent and
interconnected transportation ecosystems [4]. Smart mobility has become an integral part of daily life, with
40% of the global population traveling for at least one hour each day [5]. By integrating technologies such as
computer vision, AI, and ITS, cities can more accurately detect traffic conditions, identify vehicle types, and
Vol. 14, No. 4, August 2025, pp. 3343~3353
ISSN: 2252-8938, DOI: 10.11591/ijai.v14.i4.pp3343-3353 3343
Journal homepage: http://ijai.iaescore.com
Traffic flow prediction using long short-term memory-Komodo
Mlipir algorithm: metaheuristic optimization to multi-target
vehicle detection
Imam Ahmad Ashari
1,4
, Wahyul Amien Syafei
2
, Adi Wibowo
3
1
Doctoral Program of Information Systems, Universitas Diponegoro, Semarang, Indonesia
2
Faculty of Engineering, Universitas Diponegoro, Semarang, Indonesia
3
Faculty of Science and Mathematics, Universitas Diponegoro, Semarang, Indonesia
4
Faculty of Science and Technology, Universitas Harapan Bangsa, Purwokerto, Indonesia
Article Info ABSTRACT
Article history:
Received Dec 12, 2024
Revised Jun 24, 2025
Accepted Jul 13, 2025
Multi-target vehicle detection in urban traffic faces challenges such as poor
lighting, small object sizes, and diverse vehicle types, impacting traffic flow
prediction accuracy. This study introduces an optimized long short-term
memory (LSTM) model using the Komodo Mlipir algorithm (KMA) to
enhance prediction accuracy. Traffic video data are processed with YOLO
for vehicle classification and object counting. The LSTM model, trained to
capture traffic patterns, employs parameters optimized by KMA, including
learning rate, neuron count, and epochs. KMA integrates mutation and
crossover strategies to enable adaptive selection in global and local searches.
The model's performance was evaluated on an urban traffic dataset with
uniform configurations for population size and key LSTM parameters,
ensuring consistent evaluation. Results showed LSTM-KMA achieved a root
mean square error (RMSE) of 14.5319, outperforming LSTM (16.6827),
LSTM-improved dung beetle optimization (IDBO) (15.0946), and LSTM-
particle swarm optimization (PSO) (15.0368). Its mean absolute error
(MAE), at 8.7041, also surpassed LSTM (9.9903), LSTM-IDBO (9.0328),
and LSTM-PSO (9.0015). LSTM-KMA effectively tackles multi-target
detection challenges, improving prediction accuracy and transportation
system efficiency. This reliable solution supports real-time urban traffic
management, addressing the demands of dynamic urban environments.
Keywords:
Komodo Mlipir algorithm
Long short-term memory
Metaheuristic optimization
Multi-target vehicle detection
Traffic flow prediction
This is an open access article under the CC BY-SA license.
Corresponding Author:
Imam Ahmad Ashari
Doctoral Program of Information Systems, Universitas Diponegoro
Semarang, Indonesia
Email: [email protected]
1. INTRODUCTION
With advancements in communication technology and computer science, intelligent transportation
systems (ITS) have assumed an increasingly significant role in daily life [1]. Smart transportation has become
a cornerstone in the development of technology-based ITS to meet the evolving needs of urban societies [2].
It refers to an approach that integrates modern technology into transportation systems to enhance urban
mobility efficiency [3]. In the context of smart cities, cutting-edge technologies such as the internet of things
(IoT), data analytics, and artificial intelligence (AI) serve as foundational pillars for creating intelligent and
interconnected transportation ecosystems [4]. Smart mobility has become an integral part of daily life, with
40% of the global population traveling for at least one hour each day [5]. By integrating technologies such as
computer vision, AI, and ITS, cities can more accurately detect traffic conditions, identify vehicle types, and
ISSN: 2252-8938
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3344
predict congestion. This integration helps address urbanization challenges, such as pollution, traffic
accidents, and excessive resource consumption [6].
Traffic flow prediction is a critical element in ITS as it provides valuable insights for traffic control,
route planning, and operational management [7]. Traditional traffic flow prediction models often fail to
adequately account for the complex and dynamic characteristics of urban traffic networks [8]. With the
acceleration of urbanization and advancements in ITS, short-term traffic flow prediction has emerged as an
increasingly significant area of research [9]. Accurate predictions offer substantial benefits, including
optimized traffic planning, improved road utilization, reduced congestion, fewer traffic accidents, and
decreased environmental pollution [10].
Accurate traffic flow prediction requires the efficient extraction and analysis of large-scale urban
traffic data, including the appropriate selection of data sample sizes. Technological advancements, such as
roadside closed-circuit television (CCTV) cameras and unmanned aerial vehicles (UAVs), provide new video
data that enable more comprehensive traffic information collection through computer vision techniques [11].
These advancements support accident-based safety analysis and facilitate real-time traffic control, route
guidance, policy formulation, and more effective traffic allocation. Together, these efforts enhance traffic
efficiency and improve the quality of urban life [12]. In practice, computer vision models such as YOLO and
its advancements are widely applied to detect and analyze urban traffic conditions [13]–[18].
In ITS, traditional object detection algorithms face various challenges, particularly in dealing with
complex environments and varying lighting conditions. These challenges become more significant when
detecting small objects or analyzing multimodal data [16]. To address these limitations, enhancing data
quality and diversity through augmentation techniques is a common approach [19]. Previous research has
demonstrated that combining object detection with long short-term memory (LSTM) algorithms can
effectively predict traffic volume [20]. Additionally, studies have proposed the development of new models
leveraging and optimizing LSTM, which has proven effective in handling time-series data and improving the
accuracy of urban traffic density predictions [20]–[25]. Recent trends suggest an increasing focus on
optimizing LSTM parameters through metaheuristic approaches to improve traffic prediction performance
[22], [26]–[28]. Such an approach is anticipated to tackle the challenges of creating more reliable and
efficient predictive models for various traffic conditions.
The Komodo mlipir optimization algorithm (KMA) draws inspiration from two unique phenomena:
the behavior of Komodo dragons native to East Nusa Tenggara, Indonesia, and the traditional Javanese
walking style known as mlipir [29]. In the context of the traveling salesman problem (TSP), KMA has
exhibited superior performance compared to algorithms like the dragonfly algorithm (DKA), ant colony
optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), black hole (BH), dynamic
tabu search algorithm (DTSA), and discrete jaya algorithm (DJAYA) [30]. In our proposed research, LSTM is
combined with KMA for traffic volume prediction. The LSTM-KMA model is then compared with the
standard LSTM and other state-of-the-art combinations, namely LSTM-improved dung beetle optimization
(IDBO) and LSTM-PSO. Previous studies have shown that LSTM-IDBO outperforms methods such as gray
wolf optimization (GWO), sparrow optimization algorithm (SSA), whale optimization algorithm (WOA), and
nighthawk optimization (NGO) [26]. Similarly, LSTM-PSO has proven superior to methods like standard
LSTM, random forest regression (RFR), k-nearest regression (KNR), and decision tree regression (DTR) [28].
The main problem addressed in this study is the low accuracy in predicting complex and dynamic
traffic volumes, particularly under real-world conditions that often involve challenges such as poor lighting,
occlusions, and diverse vehicle types. To address this issue, the study aims to develop a traffic prediction
model that integrates the LSTM algorithm with the KMA as an optimization method, supported by real-time
vehicle detection data using YOLO. This research specifically focuses on how the integration of KMA can
improve the predictive accuracy of LSTM in modeling dynamic traffic volumes, and evaluates the potential
implementation of the YOLO-LSTM-KMA system under real traffic conditions. The main contribution of
this study is the development of an intelligent predictive model capable of improving traffic flow prediction
accuracy, offering both theoretical contributions in the field of optimization and time-series forecasting, and
practical contributions in supporting data-driven decision-making within ITS.
2. METHOD
2.1. Vehicle object detection
Data collection was conducted using YOLO as the object detection model for identifying vehicles in
traffic. Specifically, the YOLOv8n model was used because of its high-performance ability to detect vehicles
in complex traffic conditions. To improve detection accuracy, multi-augmentation techniques were applied,
combining scaling, zoom-in, brightness adjustment, color jitter, and noise injection. Table 1 presents the
specific values for each augmentation technique used in the study.
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3344
predict congestion. This integration helps address urbanization challenges, such as pollution, traffic
accidents, and excessive resource consumption [6].
Traffic flow prediction is a critical element in ITS as it provides valuable insights for traffic control,
route planning, and operational management [7]. Traditional traffic flow prediction models often fail to
adequately account for the complex and dynamic characteristics of urban traffic networks [8]. With the
acceleration of urbanization and advancements in ITS, short-term traffic flow prediction has emerged as an
increasingly significant area of research [9]. Accurate predictions offer substantial benefits, including
optimized traffic planning, improved road utilization, reduced congestion, fewer traffic accidents, and
decreased environmental pollution [10].
Accurate traffic flow prediction requires the efficient extraction and analysis of large-scale urban
traffic data, including the appropriate selection of data sample sizes. Technological advancements, such as
roadside closed-circuit television (CCTV) cameras and unmanned aerial vehicles (UAVs), provide new video
data that enable more comprehensive traffic information collection through computer vision techniques [11].
These advancements support accident-based safety analysis and facilitate real-time traffic control, route
guidance, policy formulation, and more effective traffic allocation. Together, these efforts enhance traffic
efficiency and improve the quality of urban life [12]. In practice, computer vision models such as YOLO and
its advancements are widely applied to detect and analyze urban traffic conditions [13]–[18].
In ITS, traditional object detection algorithms face various challenges, particularly in dealing with
complex environments and varying lighting conditions. These challenges become more significant when
detecting small objects or analyzing multimodal data [16]. To address these limitations, enhancing data
quality and diversity through augmentation techniques is a common approach [19]. Previous research has
demonstrated that combining object detection with long short-term memory (LSTM) algorithms can
effectively predict traffic volume [20]. Additionally, studies have proposed the development of new models
leveraging and optimizing LSTM, which has proven effective in handling time-series data and improving the
accuracy of urban traffic density predictions [20]–[25]. Recent trends suggest an increasing focus on
optimizing LSTM parameters through metaheuristic approaches to improve traffic prediction performance
[22], [26]–[28]. Such an approach is anticipated to tackle the challenges of creating more reliable and
efficient predictive models for various traffic conditions.
The Komodo mlipir optimization algorithm (KMA) draws inspiration from two unique phenomena:
the behavior of Komodo dragons native to East Nusa Tenggara, Indonesia, and the traditional Javanese
walking style known as mlipir [29]. In the context of the traveling salesman problem (TSP), KMA has
exhibited superior performance compared to algorithms like the dragonfly algorithm (DKA), ant colony
optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), black hole (BH), dynamic
tabu search algorithm (DTSA), and discrete jaya algorithm (DJAYA) [30]. In our proposed research, LSTM is
combined with KMA for traffic volume prediction. The LSTM-KMA model is then compared with the
standard LSTM and other state-of-the-art combinations, namely LSTM-improved dung beetle optimization
(IDBO) and LSTM-PSO. Previous studies have shown that LSTM-IDBO outperforms methods such as gray
wolf optimization (GWO), sparrow optimization algorithm (SSA), whale optimization algorithm (WOA), and
nighthawk optimization (NGO) [26]. Similarly, LSTM-PSO has proven superior to methods like standard
LSTM, random forest regression (RFR), k-nearest regression (KNR), and decision tree regression (DTR) [28].
The main problem addressed in this study is the low accuracy in predicting complex and dynamic
traffic volumes, particularly under real-world conditions that often involve challenges such as poor lighting,
occlusions, and diverse vehicle types. To address this issue, the study aims to develop a traffic prediction
model that integrates the LSTM algorithm with the KMA as an optimization method, supported by real-time
vehicle detection data using YOLO. This research specifically focuses on how the integration of KMA can
improve the predictive accuracy of LSTM in modeling dynamic traffic volumes, and evaluates the potential
implementation of the YOLO-LSTM-KMA system under real traffic conditions. The main contribution of
this study is the development of an intelligent predictive model capable of improving traffic flow prediction
accuracy, offering both theoretical contributions in the field of optimization and time-series forecasting, and
practical contributions in supporting data-driven decision-making within ITS.
2. METHOD
2.1. Vehicle object detection
Data collection was conducted using YOLO as the object detection model for identifying vehicles in
traffic. Specifically, the YOLOv8n model was used because of its high-performance ability to detect vehicles
in complex traffic conditions. To improve detection accuracy, multi-augmentation techniques were applied,
combining scaling, zoom-in, brightness adjustment, color jitter, and noise injection. Table 1 presents the
specific values for each augmentation technique used in the study.
Int J Artif Intell ISSN: 2252-8938
Traffic flow prediction using long short-term memory-Komodo Mlipir Algorithm: … (Imam Ahmad Ashari)
3345
Table 1. Augmentation values
No Aug Value Augmentation factor (image) References
1 2 3
1 Brightness adjustment Brightness factor - 0.8 1.2 [31]
2 Color jitter (Brightness, contrast,
saturation) and hue
-
Rand (0.6,1.4) and
Rand (-0.1,0.1)
Rand (0.6,1.4) and
Rand (-0.1,0.1)
[32]
3 Noise injection Gaussian noise - Rand (0, 0.1) Rand (0, 0.1) [33]
4 Scaling Scale image - Rand (0.8, 1.2) Rand (0.8, 1.2) [34]
5 Zoom in Zoom in - 1.2 1.5 [35]
In the image augmentation process summarized in Table 1, brightness adjustment was performed
with a brightness factor of 0.8 for image 2 and 1.2 for image 3. For the color jitter technique, the brightness,
contrast, and saturation factors were randomized within the range of 0.6 to 1.4, while the hue factor was
randomized between -0.1 and 0.1. Noise injection utilized Gaussian noise with values randomized between
0 and 0.1 for both images. The scaling technique was applied with a factor range of 0.8 to 1.2 for both
image 2 and image 3, while the zoom-in technique utilized a factor of 1.2 for image 2 and 1.5 for image 3.
This combination of values was designed to create significant image variations, thereby improving the
model's performance under diverse conditions.
Based on the conducted experiments, YOLOv8n outperformed YOLOv9t, achieving the highest
mAP50-95 value of 0.536. A detailed performance analysis is presented in a manuscript titled "boosting real-time
vehicle detection in urban traffic using a novel multi-augmentation". The experimental results identified the best-
performing model, named best.pt, as the foundation for the vehicle detection process in this study. The model
workflow is depicted in Figure 1, detailing the steps from data preprocessing to numeric feature extraction.
Figure 1. Numerical feature extraction process from YOLO model
The model workflow, as illustrated in Figure 1, begins with the collection of video data from traffic
CCTV recordings. This video data is processed through a preprocessing stage where it is converted into
individual frames for further analysis. Each frame is manually annotated using the Roboflow application to
label vehicle objects, which include motorcycles, cars, trucks, and buses. The annotation process involved
creating four vehicle classes and drawing bounding boxes around each object in every frame. In total, the
dataset contains 720 images with 45,347 annotations, consisting of 31,481 motorcycles, 12,402 cars,
1,184 trucks, and 280 buses. The dataset is divided into two parts: 80% for training and 20% for validation
[36], [37]. This 80:20 split is commonly used in machine learning experiments to ensure that the model has
sufficient data to learn patterns during training while maintaining an adequate portion of unseen data for
unbiased validation, allowing for accurate evaluation of the model’s generalization ability. The training
subset includes 22,136 motorcycles, 8,804 cars, 839 trucks, and 199 buses, while the validation subset
contains 9,345 motorcycles, 3,598 cars, 345 trucks, and 81 buses.
Traffic flow prediction using long short-term memory-Komodo Mlipir Algorithm: … (Imam Ahmad Ashari)
3345
Table 1. Augmentation values
No Aug Value Augmentation factor (image) References
1 2 3
1 Brightness adjustment Brightness factor - 0.8 1.2 [31]
2 Color jitter (Brightness, contrast,
saturation) and hue
-
Rand (0.6,1.4) and
Rand (-0.1,0.1)
Rand (0.6,1.4) and
Rand (-0.1,0.1)
[32]
3 Noise injection Gaussian noise - Rand (0, 0.1) Rand (0, 0.1) [33]
4 Scaling Scale image - Rand (0.8, 1.2) Rand (0.8, 1.2) [34]
5 Zoom in Zoom in - 1.2 1.5 [35]
In the image augmentation process summarized in Table 1, brightness adjustment was performed
with a brightness factor of 0.8 for image 2 and 1.2 for image 3. For the color jitter technique, the brightness,
contrast, and saturation factors were randomized within the range of 0.6 to 1.4, while the hue factor was
randomized between -0.1 and 0.1. Noise injection utilized Gaussian noise with values randomized between
0 and 0.1 for both images. The scaling technique was applied with a factor range of 0.8 to 1.2 for both
image 2 and image 3, while the zoom-in technique utilized a factor of 1.2 for image 2 and 1.5 for image 3.
This combination of values was designed to create significant image variations, thereby improving the
model's performance under diverse conditions.
Based on the conducted experiments, YOLOv8n outperformed YOLOv9t, achieving the highest
mAP50-95 value of 0.536. A detailed performance analysis is presented in a manuscript titled "boosting real-time
vehicle detection in urban traffic using a novel multi-augmentation". The experimental results identified the best-
performing model, named best.pt, as the foundation for the vehicle detection process in this study. The model
workflow is depicted in Figure 1, detailing the steps from data preprocessing to numeric feature extraction.
Figure 1. Numerical feature extraction process from YOLO model
The model workflow, as illustrated in Figure 1, begins with the collection of video data from traffic
CCTV recordings. This video data is processed through a preprocessing stage where it is converted into
individual frames for further analysis. Each frame is manually annotated using the Roboflow application to
label vehicle objects, which include motorcycles, cars, trucks, and buses. The annotation process involved
creating four vehicle classes and drawing bounding boxes around each object in every frame. In total, the
dataset contains 720 images with 45,347 annotations, consisting of 31,481 motorcycles, 12,402 cars,
1,184 trucks, and 280 buses. The dataset is divided into two parts: 80% for training and 20% for validation
[36], [37]. This 80:20 split is commonly used in machine learning experiments to ensure that the model has
sufficient data to learn patterns during training while maintaining an adequate portion of unseen data for
unbiased validation, allowing for accurate evaluation of the model’s generalization ability. The training
subset includes 22,136 motorcycles, 8,804 cars, 839 trucks, and 199 buses, while the validation subset
contains 9,345 motorcycles, 3,598 cars, 345 trucks, and 81 buses.
ISSN: 2252-8938
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3346
To increase data diversity and improve model generalization, augmentation techniques were applied
to the training dataset. These techniques include scaling, zoom-in, brightness adjustment, color jitter, and
noise injection. Each technique was applied using two parameter values, resulting in a tenfold increase in the
amount of training data. The original training dataset consists of 576 images without augmentation, while the
augmented dataset consists of 5,760 images, as shown in Table 1. The YOLO model was trained using this
enhanced dataset, and its performance was evaluated periodically using the mAP50-95 metric. If the model
did not meet the desired accuracy threshold, training was continued. Once the best-performing model was
obtained, it was used to detect vehicles in each frame and predict their classes. The detection results were
then converted into numerical features, such as vehicle counts by type, which were further processed into
traffic flow data for subsequent analysis.
2.2. Long short-term memory-Komodo Mlipir algorithm
The integration of LSTM and KMA leverages the strengths of each method in data analysis and
optimization. LSTM is highly effective at capturing temporal patterns in time-series data, making it suitable
for both short-term and long-term prediction tasks [38]. Previous studies have shown that hyperparameter
optimization using metaheuristic approaches often yields better results compared to conventional methods,
further reinforcing the advantage of combining these techniques to improve model performance [39].
The incorporation of KMA in this approach is anticipated to surpass the performance of other metaheuristic
algorithms. The integration of LSTM and KMA not only accelerates the optimization process but also
enhances the likelihood of identifying optimal hyperparameter configurations, thereby significantly
improving the performance of the LSTM model in traffic flow prediction applications. This proposed
approach is depicted in Figure 2.
Figure 2. Proposed model
Figure 2 illustrates the LSTM-KMA computation process, beginning with data reading and
preprocessing, followed by dividing the dataset into training and testing sets, allocating 80% for training and
20% for testing [40], [41]. The KMA is then initialized with specific parameters. This step involves
initializing a population of candidate solutions and applying crossover and mutation operations [42]. The
fitness of each candidate solution is evaluated to determine the suitability of the parameters for the LSTM
model using the mean absolute error (MAE) metric. The LSTM parameters being optimized include the
number of neurons, learning rate, and epochs [26]. KMA iteratively updates the candidate solutions through
an optimization loop until the optimal parameters are identified. The optimized parameters are then applied to
train the final LSTM model. The trained model is subsequently tested using the test data, with root mean
square error (RMSE) and MAE calculated as accuracy measures for the predictions. The concept of KMA in
LSTM parameter optimization is illustrated through the pseudocode presented in Algorithm 1.
Algorithm 1: Komodo Mlipir for optimizing LSTM parameters
Input:
Maximum number of iterations (�), population size (??????).
Range of LSTM parameters to be optimized (neurons, learning rate, and epochs).
Step 1: Initialization
Initialize a population of ?????? individuals (komodo) with random combinations of LSTM
parameters.
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3346
To increase data diversity and improve model generalization, augmentation techniques were applied
to the training dataset. These techniques include scaling, zoom-in, brightness adjustment, color jitter, and
noise injection. Each technique was applied using two parameter values, resulting in a tenfold increase in the
amount of training data. The original training dataset consists of 576 images without augmentation, while the
augmented dataset consists of 5,760 images, as shown in Table 1. The YOLO model was trained using this
enhanced dataset, and its performance was evaluated periodically using the mAP50-95 metric. If the model
did not meet the desired accuracy threshold, training was continued. Once the best-performing model was
obtained, it was used to detect vehicles in each frame and predict their classes. The detection results were
then converted into numerical features, such as vehicle counts by type, which were further processed into
traffic flow data for subsequent analysis.
2.2. Long short-term memory-Komodo Mlipir algorithm
The integration of LSTM and KMA leverages the strengths of each method in data analysis and
optimization. LSTM is highly effective at capturing temporal patterns in time-series data, making it suitable
for both short-term and long-term prediction tasks [38]. Previous studies have shown that hyperparameter
optimization using metaheuristic approaches often yields better results compared to conventional methods,
further reinforcing the advantage of combining these techniques to improve model performance [39].
The incorporation of KMA in this approach is anticipated to surpass the performance of other metaheuristic
algorithms. The integration of LSTM and KMA not only accelerates the optimization process but also
enhances the likelihood of identifying optimal hyperparameter configurations, thereby significantly
improving the performance of the LSTM model in traffic flow prediction applications. This proposed
approach is depicted in Figure 2.
Figure 2. Proposed model
Figure 2 illustrates the LSTM-KMA computation process, beginning with data reading and
preprocessing, followed by dividing the dataset into training and testing sets, allocating 80% for training and
20% for testing [40], [41]. The KMA is then initialized with specific parameters. This step involves
initializing a population of candidate solutions and applying crossover and mutation operations [42]. The
fitness of each candidate solution is evaluated to determine the suitability of the parameters for the LSTM
model using the mean absolute error (MAE) metric. The LSTM parameters being optimized include the
number of neurons, learning rate, and epochs [26]. KMA iteratively updates the candidate solutions through
an optimization loop until the optimal parameters are identified. The optimized parameters are then applied to
train the final LSTM model. The trained model is subsequently tested using the test data, with root mean
square error (RMSE) and MAE calculated as accuracy measures for the predictions. The concept of KMA in
LSTM parameter optimization is illustrated through the pseudocode presented in Algorithm 1.
Algorithm 1: Komodo Mlipir for optimizing LSTM parameters
Input:
Maximum number of iterations (�), population size (??????).
Range of LSTM parameters to be optimized (neurons, learning rate, and epochs).
Step 1: Initialization
Initialize a population of ?????? individuals (komodo) with random combinations of LSTM
parameters.
Int J Artif Intell ISSN: 2252-8938
Traffic flow prediction using long short-term memory-Komodo Mlipir Algorithm: … (Imam Ahmad Ashari)
3347
Each individual ?????? in the population is represented as ???????????? = [�??????,�??????,�??????], where �??????,�??????, and �??????
respectively denote neurons, learning rate, and epochs.
Step 2: Fitness Evaluation
Evaluate the initial fitness of each candidate solution by measuring the LSTM’s
performance on the validation dataset.
Use the objective function: Minimize F=MAE.
Sort the individuals based on their fitness scores and categorize them into three
groups:
- Large males (elite, top performers)
- Females (moderate performance)
- Small males (low performers)
Step 3: Main Loop
�ℎ?????????????????? (� ≤ �):
1. Reassess each individual's fitness score.
2. Update their positions as follows:
- Large males: Adjust positions using exploitation strategies.
- Females:
- Mate with the top-performing large male using exploitation method.
- Reproduce asexually via parthenogenesis using exploration strategies.
- Small males: Explore the solution space randomly using exploration
strategies.
3. Apply selection process:
- Retain the best-performing individual (elitism).
- Improve weaker individuals using update strategy in equation.
4. Increment the iteration count (� = � + 1).
End While
Step 4: Output the Best Solution
Output the best LSTM parameters ( ??????_????????????��) and the best fitness value ( �_????????????��).
Output:
Optimal LSTM parameters
Algorithm 1 is the pseudocode of the KMA used to optimize the parameters of the LSTM model.
This algorithm aims to find the best combination of neurons, learning rate, and number of epochs by
minimizing the MAE. The step-by-step procedure is outlined in the pseudocode above. To facilitate
understanding, the workflow of this algorithm is also illustrated in the flowchart, as shown in Figure 3.
Figure 3. Optimization workflow of the LSTM model using KMA
Traffic flow prediction using long short-term memory-Komodo Mlipir Algorithm: … (Imam Ahmad Ashari)
3347
Each individual ?????? in the population is represented as ???????????? = [�??????,�??????,�??????], where �??????,�??????, and �??????
respectively denote neurons, learning rate, and epochs.
Step 2: Fitness Evaluation
Evaluate the initial fitness of each candidate solution by measuring the LSTM’s
performance on the validation dataset.
Use the objective function: Minimize F=MAE.
Sort the individuals based on their fitness scores and categorize them into three
groups:
- Large males (elite, top performers)
- Females (moderate performance)
- Small males (low performers)
Step 3: Main Loop
�ℎ?????????????????? (� ≤ �):
1. Reassess each individual's fitness score.
2. Update their positions as follows:
- Large males: Adjust positions using exploitation strategies.
- Females:
- Mate with the top-performing large male using exploitation method.
- Reproduce asexually via parthenogenesis using exploration strategies.
- Small males: Explore the solution space randomly using exploration
strategies.
3. Apply selection process:
- Retain the best-performing individual (elitism).
- Improve weaker individuals using update strategy in equation.
4. Increment the iteration count (� = � + 1).
End While
Step 4: Output the Best Solution
Output the best LSTM parameters ( ??????_????????????��) and the best fitness value ( �_????????????��).
Output:
Optimal LSTM parameters
Algorithm 1 is the pseudocode of the KMA used to optimize the parameters of the LSTM model.
This algorithm aims to find the best combination of neurons, learning rate, and number of epochs by
minimizing the MAE. The step-by-step procedure is outlined in the pseudocode above. To facilitate
understanding, the workflow of this algorithm is also illustrated in the flowchart, as shown in Figure 3.
Figure 3. Optimization workflow of the LSTM model using KMA
ISSN: 2252-8938
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3348
3. RESULTS AND DISCUSSION
3.1. Data and environment
In this study, the data used for traffic flow prediction was collected from CCTV cameras installed in
Fatmawati, Semarang City. The data collection period spanned from December 19, 2023, to February 15, 2024.
Data was gathered by extracting images from recorded videos at 5-minute intervals. The frame interval was
determined by multiplying the frames per second (FPS) by 60 and the specified number of minutes. With an
FPS of 25, the resulting frame interval was 25×60×5=7,500 frames. This means the program extracted one
image for every 7,500 frames. From this extraction process, a total of 720 images were obtained. A total of
45,347 annotations were generated from these images, comprising 31,481 motorcycles, 12,402 cars,
1,184 trucks, and 280 buses. In addition to vehicle types, date and time information was also extracted from
the dataset. The dataset was then divided into two subsets: a training subset and a validation subset, to
facilitate model training and evaluation. The distribution of vehicle annotations in the training set includes
22,136 motorcycles, 8,804 cars, 839 trucks, and 199 buses. The validation set consists of 9,345 motorcycles,
3,598 cars, 345 trucks, and 81 buses. This structured data collection and preprocessing process provides a
solid foundation for developing traffic flow prediction models, ensuring that the dataset is representative and
well-annotated for effective model training and evaluation.
This study utilized Google Colab Pro for experimental configuration. Google Colab offers cloud-
based and open-source computing services to handle the extensive processing requirements needed for model
training [43]. The runtime environment included Python 3 and an NVIDIA T4 GPU. The programming
language utilized was Python 3.10.12, and the PyTorch framework version 2.3.0 was implemented with
CUDA version 12.1 support.
3.2. Parameter settings and model optimization
In this study, the parameters for the metaheuristic method were standardized by setting the
population size to 30, as referenced in previous studies [26]. The parameter ranges optimized for the LSTM
model include the number of neurons (300-500), learning rate (0.001-0.01), and number of epochs (1-150).
These ranges were initially adopted based on prior literature and then further refined through multiple
trial-and-error experiments to obtain optimal performance. For the conventional LSTM model, the analysis
was conducted using the highest values in each range-500 neurons, a learning rate of 0.01, and 150 epochs.
Detailed configurations of other parameters used for each model can be found in Table 2.
Table 2 presents the parameter settings used for various algorithms in optimizing the LSTM model.
For LSTM-KMA, the size of the population involved in the selection process is set to 10. In the LSTM-IDBO
algorithm, the coefficient of variation is set to 0.1, and the scaling parameter for balancing exploration and
exploitation is set to 0.5. The LSTM-PSO algorithm uses a self-learning factor of 1.5 and a group learning
factor of 2.
Table 2. Parameter setting of the various algorithms
Algorithm Parameters Settings Reference
LSTM-KMA Size of population involved in selection 10 [42]
IDBO-LSTM Coefficient of variation
Scale or parameter for setting exploration and exploitation
0.1
0.5
[26]
LSTM-PSO Self-learning factor
Group learning factor
1.5
2
[28]
3.3. Evaluation criteria
The appropriate performance evaluation metrics for continuous data obtained in real-time are
regression loss functions [44]. Therefore, the performance evaluation metrics used in this study are RMSE
and MAE. RMSE reflects the degree of deviation of predicted values from actual values. The formula for
RMSE is provided in (1) [45]. MAE represents the mean of absolute errors, where absolute error is the
difference between predicted and actual values. A low MAE value indicates that the model predicts values
close to the actual values. The formula for MAE is provided in (2) [26].
�??????��= √
1
??????
∑|??????
??????−??????
??????̂|
??????
??????=1
2
(1)
????????????�=
1
??????
∑|??????
??????−??????
??????̂|
??????
??????=1 (2)
RMSE and MAE are two evaluation metrics used to measure the prediction errors of a model.
In the RMSE formula, the difference between the actual value (??????
??????) and the predicted value (??????
??????̂) is squared to
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3348
3. RESULTS AND DISCUSSION
3.1. Data and environment
In this study, the data used for traffic flow prediction was collected from CCTV cameras installed in
Fatmawati, Semarang City. The data collection period spanned from December 19, 2023, to February 15, 2024.
Data was gathered by extracting images from recorded videos at 5-minute intervals. The frame interval was
determined by multiplying the frames per second (FPS) by 60 and the specified number of minutes. With an
FPS of 25, the resulting frame interval was 25×60×5=7,500 frames. This means the program extracted one
image for every 7,500 frames. From this extraction process, a total of 720 images were obtained. A total of
45,347 annotations were generated from these images, comprising 31,481 motorcycles, 12,402 cars,
1,184 trucks, and 280 buses. In addition to vehicle types, date and time information was also extracted from
the dataset. The dataset was then divided into two subsets: a training subset and a validation subset, to
facilitate model training and evaluation. The distribution of vehicle annotations in the training set includes
22,136 motorcycles, 8,804 cars, 839 trucks, and 199 buses. The validation set consists of 9,345 motorcycles,
3,598 cars, 345 trucks, and 81 buses. This structured data collection and preprocessing process provides a
solid foundation for developing traffic flow prediction models, ensuring that the dataset is representative and
well-annotated for effective model training and evaluation.
This study utilized Google Colab Pro for experimental configuration. Google Colab offers cloud-
based and open-source computing services to handle the extensive processing requirements needed for model
training [43]. The runtime environment included Python 3 and an NVIDIA T4 GPU. The programming
language utilized was Python 3.10.12, and the PyTorch framework version 2.3.0 was implemented with
CUDA version 12.1 support.
3.2. Parameter settings and model optimization
In this study, the parameters for the metaheuristic method were standardized by setting the
population size to 30, as referenced in previous studies [26]. The parameter ranges optimized for the LSTM
model include the number of neurons (300-500), learning rate (0.001-0.01), and number of epochs (1-150).
These ranges were initially adopted based on prior literature and then further refined through multiple
trial-and-error experiments to obtain optimal performance. For the conventional LSTM model, the analysis
was conducted using the highest values in each range-500 neurons, a learning rate of 0.01, and 150 epochs.
Detailed configurations of other parameters used for each model can be found in Table 2.
Table 2 presents the parameter settings used for various algorithms in optimizing the LSTM model.
For LSTM-KMA, the size of the population involved in the selection process is set to 10. In the LSTM-IDBO
algorithm, the coefficient of variation is set to 0.1, and the scaling parameter for balancing exploration and
exploitation is set to 0.5. The LSTM-PSO algorithm uses a self-learning factor of 1.5 and a group learning
factor of 2.
Table 2. Parameter setting of the various algorithms
Algorithm Parameters Settings Reference
LSTM-KMA Size of population involved in selection 10 [42]
IDBO-LSTM Coefficient of variation
Scale or parameter for setting exploration and exploitation
0.1
0.5
[26]
LSTM-PSO Self-learning factor
Group learning factor
1.5
2
[28]
3.3. Evaluation criteria
The appropriate performance evaluation metrics for continuous data obtained in real-time are
regression loss functions [44]. Therefore, the performance evaluation metrics used in this study are RMSE
and MAE. RMSE reflects the degree of deviation of predicted values from actual values. The formula for
RMSE is provided in (1) [45]. MAE represents the mean of absolute errors, where absolute error is the
difference between predicted and actual values. A low MAE value indicates that the model predicts values
close to the actual values. The formula for MAE is provided in (2) [26].
�??????��= √
1
??????
∑|??????
??????−??????
??????̂|
??????
??????=1
2
(1)
????????????�=
1
??????
∑|??????
??????−??????
??????̂|
??????
??????=1 (2)
RMSE and MAE are two evaluation metrics used to measure the prediction errors of a model.
In the RMSE formula, the difference between the actual value (??????
??????) and the predicted value (??????
??????̂) is squared to
Int J Artif Intell ISSN: 2252-8938
Traffic flow prediction using long short-term memory-Komodo Mlipir Algorithm: … (Imam Ahmad Ashari)
3349
calculate (??????
??????−??????
??????̂)
2
, giving greater weight to larger errors, and then the square root is taken. RMSE provides
additional insights by reflecting the degree of deviation between predicted values and actual values,
being more sensitive to large errors. MAE has a similar formula but with a different approach. In this
formula, ?????? represents the total number of data points or observations in the dataset, indicating the number of
data points analyzed. ??????
?????? is the actual value of the ??????-th data point, representing the true data to be predicted,
such as the actual number of vehicles in traffic prediction. On the other hand, ??????
??????̂ is the predicted value
generated by the model for the ??????-th data point, reflecting the estimated number of vehicles. The absolute
difference between actual and predicted values is calculated as |??????
??????−??????
??????̂|, providing an error measure without
regard to error direction. All these absolute differences are summed and divided by the total number of data
points (??????) to yield the MAE.
Therefore, RMSE and MAE provide an overall measure of how close the model's predictions are to
the actual values. The prediction results of the LSTM, LSTM-KMA, LSTM-IDBO, and LSTM-PSO models
are compared with the actual data. The prediction outcomes of the utilized models are shown in Figure 4.
Figure 4 illustrates the comparison between the actual traffic flow data (TRUE) and the predicted results
from several models, namely LSTM, LSTM-KMA, LSTM-IDBO, and LSTM-PSO. The graph shows how
each model's predictions align with or deviate from the actual traffic flow values over time, highlighting the
accuracy and performance differences among the models.
Figure 4. Prediction results of each model
3.4. Results and performance analysis
The developed model, LSTM-KMA, is compared with the baseline LSTM model. In addition, two
other LSTM models optimized using metaheuristic algorithms, namely LSTM-IDBO and LSTM-PSO, are
also included in the comparison. The performance of each model based on the RMSE is shown in Figure 5. A
separate comparison using the MAE metric is presented in Figure 6. This figure highlights the average
prediction error for each model. The lower the MAE value, the closer the model's predictions are to the actual
data. To support the visual comparison, both RMSE and MAE values are summarized in Table 3. This table
provides a clearer view of each model’s numerical performance. It complements the graphical results shown
in the Figures 5 and 6.
Figure 5. RMSE comparison of models
Figure 6. MAE comparison of models
0
20
40
60
80
100
120
140
160
07:05:00 06:00:00 06:15:00 06:30:00 06:45:00 06:00:00 06:15:00 06:30:00 06:45:00 06:00:00 06:15:00 06:30:00 06:45:00 07:00:00 06:05:00 06:20:00 06:35:00 06:50:00 06:05:00 06:20:00 06:35:00 06:50:00 06:00:00 06:15:00 06:30:00 06:45:00 07:00:00 06:10:00 06:25:00 06:40:00 06:55:00 06:00:00 06:15:00 06:30:00 06:45:00 06:00:00 06:15:00 06:30:00 06:45:00 07:00:00 06:00:00 06:15:00 06:30:00 06:45:00 06:00:00 06:15:00 06:30:00 06:45:00
Traffic Flow Vehicle
Time/5 min
TRUE LSTM LSTM-KMA LSTM-IDBO LSTM-PSO
Traffic flow prediction using long short-term memory-Komodo Mlipir Algorithm: … (Imam Ahmad Ashari)
3349
calculate (??????
??????−??????
??????̂)
2
, giving greater weight to larger errors, and then the square root is taken. RMSE provides
additional insights by reflecting the degree of deviation between predicted values and actual values,
being more sensitive to large errors. MAE has a similar formula but with a different approach. In this
formula, ?????? represents the total number of data points or observations in the dataset, indicating the number of
data points analyzed. ??????
?????? is the actual value of the ??????-th data point, representing the true data to be predicted,
such as the actual number of vehicles in traffic prediction. On the other hand, ??????
??????̂ is the predicted value
generated by the model for the ??????-th data point, reflecting the estimated number of vehicles. The absolute
difference between actual and predicted values is calculated as |??????
??????−??????
??????̂|, providing an error measure without
regard to error direction. All these absolute differences are summed and divided by the total number of data
points (??????) to yield the MAE.
Therefore, RMSE and MAE provide an overall measure of how close the model's predictions are to
the actual values. The prediction results of the LSTM, LSTM-KMA, LSTM-IDBO, and LSTM-PSO models
are compared with the actual data. The prediction outcomes of the utilized models are shown in Figure 4.
Figure 4 illustrates the comparison between the actual traffic flow data (TRUE) and the predicted results
from several models, namely LSTM, LSTM-KMA, LSTM-IDBO, and LSTM-PSO. The graph shows how
each model's predictions align with or deviate from the actual traffic flow values over time, highlighting the
accuracy and performance differences among the models.
Figure 4. Prediction results of each model
3.4. Results and performance analysis
The developed model, LSTM-KMA, is compared with the baseline LSTM model. In addition, two
other LSTM models optimized using metaheuristic algorithms, namely LSTM-IDBO and LSTM-PSO, are
also included in the comparison. The performance of each model based on the RMSE is shown in Figure 5. A
separate comparison using the MAE metric is presented in Figure 6. This figure highlights the average
prediction error for each model. The lower the MAE value, the closer the model's predictions are to the actual
data. To support the visual comparison, both RMSE and MAE values are summarized in Table 3. This table
provides a clearer view of each model’s numerical performance. It complements the graphical results shown
in the Figures 5 and 6.
Figure 5. RMSE comparison of models
Figure 6. MAE comparison of models
0
20
40
60
80
100
120
140
160
07:05:00 06:00:00 06:15:00 06:30:00 06:45:00 06:00:00 06:15:00 06:30:00 06:45:00 06:00:00 06:15:00 06:30:00 06:45:00 07:00:00 06:05:00 06:20:00 06:35:00 06:50:00 06:05:00 06:20:00 06:35:00 06:50:00 06:00:00 06:15:00 06:30:00 06:45:00 07:00:00 06:10:00 06:25:00 06:40:00 06:55:00 06:00:00 06:15:00 06:30:00 06:45:00 06:00:00 06:15:00 06:30:00 06:45:00 07:00:00 06:00:00 06:15:00 06:30:00 06:45:00 06:00:00 06:15:00 06:30:00 06:45:00
Traffic Flow Vehicle
Time/5 min
TRUE LSTM LSTM-KMA LSTM-IDBO LSTM-PSO
ISSN: 2252-8938
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3350
Table 3. The RMSE and MAE values of the models were evaluated individually
Model RMSE MAE
LSTM 16.6827 9.9903
LSTM-KMA 14.5319 8.7041
LSTM-IDBO 15.0946 9.0328
LSTM-PSO 15.0368 9.0015
Table 3 shows that the LSTM-KMA model achieves the lowest RMSE value of 14.5319, indicating
the best performance compared to the other models. In contrast, the baseline LSTM model records the highest
RMSE value of 16.6827, indicating the lowest prediction accuracy. The LSTM-IDBO and LSTM-PSO models
achieve RMSE values of 15.0946 and 15.0368, respectively, demonstrating improved performance compared
to the baseline LSTM but still falling short of LSTM-KMA. In terms of MAE, LSTM-KMA also demonstrates
the best performance with the lowest value of 8.7041, compared to the baseline LSTM (9.9903), LSTM-IDBO
(9.0328), and LSTM-PSO (9.0015). Based on this analysis, optimization using the KMA has proven to be the
most effective method for enhancing the performance of the LSTM model in terms of both RMSE and MAE,
making it the recommended approach in this study.
3.5. Challenges
Object detection using YOLO on this dataset faces several key challenges, primarily due to
variations in lighting and traffic density. Light reflections, poor illumination, and high traffic congestion
significantly reduce detection accuracy. Additionally, the presence of multiple object types in a single frame
such as motorcycles, cars, trucks, and buses makes it difficult for the model to distinguish overlapping
objects, especially for less dominant classes like trucks and buses. Meanwhile, the use of metaheuristic
algorithms to optimize LSTM parameters yields better prediction accuracy. However, the drawback lies in
the longer runtime compared to conventional methods, as the search for optimal parameters involves
complex and iterative processes.
3.6. Practical implications for intelligent transportation systems deployment
The proposed YOLO-LSTM-KMA framework demonstrates promising potential for real-world
deployment in ITS. By integrating real-time object detection with time-series traffic prediction, this approach
supports automated traffic monitoring and data-driven decision-making. However, several practical aspects
must be considered:
‒ Scalability: the framework is designed to handle large volumes of traffic video data, making it suitable
for deployment in urban environments with high traffic density. However, the annotation and training
process still require considerable effort, which may need automation or semi-supervised techniques for
broader scalability.
‒ Computational requirements: real-time detection using YOLO and prediction with LSTM-KMA
demands sufficient computational resources, particularly during model training and optimization.
Deployment in the field would require edge computing or cloud-based infrastructure to meet latency
constraints, especially for continuous traffic flow analysis.
‒ Integration challenges: integrating this model into existing ITS infrastructure may involve challenges
such as data compatibility, synchronization across sensors and cameras, and ensuring reliability in
variable conditions (e.g., weather, lighting, and occlusion). Robust preprocessing and adaptive
retraining strategies could help mitigate these issues.
3.7. Comparison with related studies
The results of this study were compared with several existing approaches in the literature:
‒ Baseline and conventional models: traditional LSTM models often struggle with optimizing
hyperparameters effectively, leading to suboptimal predictions. The integration of KMA in this study
outperforms the standard LSTM by achieving higher accuracy and better generalization, particularly
under complex traffic scenarios.
‒ Metaheuristic-based models: compared to other metaheuristic-integrated models such as LSTM-IDBO
and LSTM-PSO, the proposed LSTM-KMA model provides competitive or superior performance in
terms of prediction accuracy. However, like other metaheuristic approaches, it incurs a higher
computational cost due to its iterative search mechanism.
‒ Advancements and differences: unlike previous works that focus either on detection or prediction alone,
this study presents a complete pipeline from real-time vehicle detection to traffic flow prediction. This
integrated design contributes to improved performance and practical applicability for ITS, aligning with
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3350
Table 3. The RMSE and MAE values of the models were evaluated individually
Model RMSE MAE
LSTM 16.6827 9.9903
LSTM-KMA 14.5319 8.7041
LSTM-IDBO 15.0946 9.0328
LSTM-PSO 15.0368 9.0015
Table 3 shows that the LSTM-KMA model achieves the lowest RMSE value of 14.5319, indicating
the best performance compared to the other models. In contrast, the baseline LSTM model records the highest
RMSE value of 16.6827, indicating the lowest prediction accuracy. The LSTM-IDBO and LSTM-PSO models
achieve RMSE values of 15.0946 and 15.0368, respectively, demonstrating improved performance compared
to the baseline LSTM but still falling short of LSTM-KMA. In terms of MAE, LSTM-KMA also demonstrates
the best performance with the lowest value of 8.7041, compared to the baseline LSTM (9.9903), LSTM-IDBO
(9.0328), and LSTM-PSO (9.0015). Based on this analysis, optimization using the KMA has proven to be the
most effective method for enhancing the performance of the LSTM model in terms of both RMSE and MAE,
making it the recommended approach in this study.
3.5. Challenges
Object detection using YOLO on this dataset faces several key challenges, primarily due to
variations in lighting and traffic density. Light reflections, poor illumination, and high traffic congestion
significantly reduce detection accuracy. Additionally, the presence of multiple object types in a single frame
such as motorcycles, cars, trucks, and buses makes it difficult for the model to distinguish overlapping
objects, especially for less dominant classes like trucks and buses. Meanwhile, the use of metaheuristic
algorithms to optimize LSTM parameters yields better prediction accuracy. However, the drawback lies in
the longer runtime compared to conventional methods, as the search for optimal parameters involves
complex and iterative processes.
3.6. Practical implications for intelligent transportation systems deployment
The proposed YOLO-LSTM-KMA framework demonstrates promising potential for real-world
deployment in ITS. By integrating real-time object detection with time-series traffic prediction, this approach
supports automated traffic monitoring and data-driven decision-making. However, several practical aspects
must be considered:
‒ Scalability: the framework is designed to handle large volumes of traffic video data, making it suitable
for deployment in urban environments with high traffic density. However, the annotation and training
process still require considerable effort, which may need automation or semi-supervised techniques for
broader scalability.
‒ Computational requirements: real-time detection using YOLO and prediction with LSTM-KMA
demands sufficient computational resources, particularly during model training and optimization.
Deployment in the field would require edge computing or cloud-based infrastructure to meet latency
constraints, especially for continuous traffic flow analysis.
‒ Integration challenges: integrating this model into existing ITS infrastructure may involve challenges
such as data compatibility, synchronization across sensors and cameras, and ensuring reliability in
variable conditions (e.g., weather, lighting, and occlusion). Robust preprocessing and adaptive
retraining strategies could help mitigate these issues.
3.7. Comparison with related studies
The results of this study were compared with several existing approaches in the literature:
‒ Baseline and conventional models: traditional LSTM models often struggle with optimizing
hyperparameters effectively, leading to suboptimal predictions. The integration of KMA in this study
outperforms the standard LSTM by achieving higher accuracy and better generalization, particularly
under complex traffic scenarios.
‒ Metaheuristic-based models: compared to other metaheuristic-integrated models such as LSTM-IDBO
and LSTM-PSO, the proposed LSTM-KMA model provides competitive or superior performance in
terms of prediction accuracy. However, like other metaheuristic approaches, it incurs a higher
computational cost due to its iterative search mechanism.
‒ Advancements and differences: unlike previous works that focus either on detection or prediction alone,
this study presents a complete pipeline from real-time vehicle detection to traffic flow prediction. This
integrated design contributes to improved performance and practical applicability for ITS, aligning with
Int J Artif Intell ISSN: 2252-8938
Traffic flow prediction using long short-term memory-Komodo Mlipir Algorithm: … (Imam Ahmad Ashari)
3351
the direction of recent studies while introducing a novel optimization algorithm tailored to traffic data
characteristics.
Overall, the study contributes to bridging the gap between academic models and real-world ITS
implementation by addressing both detection accuracy and prediction robustness, while acknowledging the
trade-offs in computation and integration complexity.
4. CONCLUSION
Multi-target vehicle detection in urban traffic faces significant challenges, including poor lighting,
small object sizes, and variations in vehicle types, all of which affect the accuracy of traffic flow predictions.
To address these challenges, this study proposes the use of a LSTM model optimized with the KMA. The
analysis results show that the LSTM-KMA model achieves the lowest RMSE of 14.5319, outperforming the
baseline LSTM (16.6827), LSTM-IDBO (15.0946), and LSTM-PSO (15.0368). Furthermore, LSTM-KMA
also delivers the best performance based on the MAE, with the lowest value of 8.7041, superior to the
baseline LSTM (9.9903), LSTM-IDBO (9.0328), and LSTM-PSO (9.0015). This demonstrates that
optimization using KMA significantly improves the accuracy of the LSTM model's predictions when
addressing the complexity of multi-target vehicle detection in urban traffic. Thus, this research makes a
significant contribution to the development of predictive models that not only address the challenges in
multi-target vehicle detection but also support real-time traffic management systems.
ACKNOWLEDGEMENTS
We would like to express our gratitude to the Department of Transportation of Semarang City for
their assistance and support in providing the valuable dataset used in this research.
FUNDING INFORMATION
The authors affirm that this research was conducted with personal funding and did not receive any
financial support from external institutions, funding agencies, or grant programs. All expenses related to the
execution of this study were borne solely by the authors.
AUTHOR CONTRIBUTIONS STATEMENT
This journal uses the Contributor Roles Taxonomy (CRediT) to recognize individual author
contributions, reduce authorship disputes, and facilitate collaboration.
Name of Author C M So Va Fo I R D O E Vi Su P Fu
Imam Ahmad Ashari ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Wahyul Amien Syafei ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Adi Wibowo ✓ ✓ ✓
C : Conceptualization
M : Methodology
So : Software
Va : Validation
Fo : Formal analysis
I : Investigation
R : Resources
D : Data Curation
O : Writing - Original Draft
E : Writing - Review & Editing
Vi : Visualization
Su : Supervision
P : Project administration
Fu : Funding acquisition
CONFLICT OF INTEREST STATEMENT
The authors declare that they have no known competing financial interests, personal relationships, or
non-financial competing interests that could have appeared to influence the work reported in this paper.
Authors state no conflict of interest.
DATA AVAILABILITY
The data that support the findings of this study were obtained from the Department of
Transportation of Semarang City. Restrictions apply to the availability of these data, which were used under
permission for this study. Data are available from the corresponding author upon reasonable request and with
the permission of the Department of Transportation of Semarang City.
Traffic flow prediction using long short-term memory-Komodo Mlipir Algorithm: … (Imam Ahmad Ashari)
3351
the direction of recent studies while introducing a novel optimization algorithm tailored to traffic data
characteristics.
Overall, the study contributes to bridging the gap between academic models and real-world ITS
implementation by addressing both detection accuracy and prediction robustness, while acknowledging the
trade-offs in computation and integration complexity.
4. CONCLUSION
Multi-target vehicle detection in urban traffic faces significant challenges, including poor lighting,
small object sizes, and variations in vehicle types, all of which affect the accuracy of traffic flow predictions.
To address these challenges, this study proposes the use of a LSTM model optimized with the KMA. The
analysis results show that the LSTM-KMA model achieves the lowest RMSE of 14.5319, outperforming the
baseline LSTM (16.6827), LSTM-IDBO (15.0946), and LSTM-PSO (15.0368). Furthermore, LSTM-KMA
also delivers the best performance based on the MAE, with the lowest value of 8.7041, superior to the
baseline LSTM (9.9903), LSTM-IDBO (9.0328), and LSTM-PSO (9.0015). This demonstrates that
optimization using KMA significantly improves the accuracy of the LSTM model's predictions when
addressing the complexity of multi-target vehicle detection in urban traffic. Thus, this research makes a
significant contribution to the development of predictive models that not only address the challenges in
multi-target vehicle detection but also support real-time traffic management systems.
ACKNOWLEDGEMENTS
We would like to express our gratitude to the Department of Transportation of Semarang City for
their assistance and support in providing the valuable dataset used in this research.
FUNDING INFORMATION
The authors affirm that this research was conducted with personal funding and did not receive any
financial support from external institutions, funding agencies, or grant programs. All expenses related to the
execution of this study were borne solely by the authors.
AUTHOR CONTRIBUTIONS STATEMENT
This journal uses the Contributor Roles Taxonomy (CRediT) to recognize individual author
contributions, reduce authorship disputes, and facilitate collaboration.
Name of Author C M So Va Fo I R D O E Vi Su P Fu
Imam Ahmad Ashari ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Wahyul Amien Syafei ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Adi Wibowo ✓ ✓ ✓
C : Conceptualization
M : Methodology
So : Software
Va : Validation
Fo : Formal analysis
I : Investigation
R : Resources
D : Data Curation
O : Writing - Original Draft
E : Writing - Review & Editing
Vi : Visualization
Su : Supervision
P : Project administration
Fu : Funding acquisition
CONFLICT OF INTEREST STATEMENT
The authors declare that they have no known competing financial interests, personal relationships, or
non-financial competing interests that could have appeared to influence the work reported in this paper.
Authors state no conflict of interest.
DATA AVAILABILITY
The data that support the findings of this study were obtained from the Department of
Transportation of Semarang City. Restrictions apply to the availability of these data, which were used under
permission for this study. Data are available from the corresponding author upon reasonable request and with
the permission of the Department of Transportation of Semarang City.
ISSN: 2252-8938
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3352
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doi: 10.1016/j.engappai.2024.109125.
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10.1016/j.aap.2023.107191.
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Electronics, vol. 12, no. 17, 2023, doi: 10.3390/electronics12173664.
[19] I. Shamta, F. Demir, and B. E. Demir, “Predictive fault detection and resolution using YOLOv8 segmentation model: a
comprehensive study on hotspot faults and generalization challenges in computer vision,” Ain Shams Engineering Journal,
vol. 15, no. 12, 2024, doi: 10.1016/j.asej.2024.103148.
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for short-term traffic flow prediction,” Physica A: Statistical Mechanics and its Applications, vol. 583, 2021,
doi: 10.1016/j.physa.2021.126293.
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Measurement: Sensors, vol. 28, 2023, doi: 10.1016/j.measen.2023.100843.
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traffic flow prediction,” Physica A: Statistical Mechanics and its Applications, vol. 634, 2024, doi: 10.1016/j.physa.2023.129448.
[23] J. D. Wang and C. O. N. Susanto, “Traffic flow prediction with heterogenous data using a hybrid CNN-LSTM model,”
Computers, Materials and Continua, vol. 76, no. 3, pp. 3097–3112, 2023, doi: 10.32604/cmc.2023.040914.
[24] A. R. Sattarzadeh, R. J. Kutadinata, P. N. Pathirana, and V. T. Huynh, “A novel hybrid deep learning model with ARIMA conv-
LSTM networks and shuffle attention layer for short-term traffic flow prediction,” Transportmetrica A: Transport Science,
vol. 21, no. 1, pp. 388–410, 2025, doi: 10.1080/23249935.2023.2236724.
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Neural Networks, vol. 171, pp. 251–262, 2024, doi: 10.1016/j.neunet.2023.12.016.
[26] K. Zhao, D. Guo, M. Sun, C. Zhao, and H. Shuai, “Short-term traffic flow prediction based on VMD and IDBO-LSTM,” IEEE
Access, vol. 11, pp. 97072–97088, 2023, doi: 10.1109/ACCESS.2023.3312711.
[27] Bharti, P. Redhu, and K. Kumar, “Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-
LSTM,” Physica A: Statistical Mechanics and its Applications, vol. 625, 2023, doi: 10.1016/j.physa.2023.129001.
[28] C. Chaoura, H. Lazar, and Z. Jarir, “Traffic flow prediction at intersections: enhancing with a Hybrid LSTM-PSO approach,”
International Journal of Advanced Computer Science and Applications, vol. 15, no. 5, pp. 494–501, 2024,
doi: 10.14569/IJACSA.2024.0150549.
[29] S. Suyanto, A. A. Ariyanto, and A. F. Ariyanto, “Komodo Mlipir algorithm,” Applied Soft Computing, vol. 114, 2022,
doi: 10.1016/j.asoc.2021.108043.
[30] G. K. Jati, G. Kuwanto, T. Hashmi, and H. Widjaja, “Discrete komodo algorithm for traveling salesman problem,” Applied Soft
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samples,” International Journal of Computational Intelligence Systems, vol. 16, no. 1, 2023, doi: 10.1007/s44196-023-00328-0.
Int J Artif Intell, Vol. 14, No. 4, August 2025: 3343-3353
3352
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prediction for road networks under bad weather,” Engineering Applications of Artificial Intelligence, vol. 137, 2024,
doi: 10.1016/j.engappai.2024.109125.
[2] D. Oladimeji, K. Gupta, N. A. Kose, K. Gundogan, L. Ge, and F. Liang, “Smart transportation: an overview of technologies and
applications,” Sensors, vol. 23, no. 8, pp. 1–32, 2023, doi: 10.3390/s23083880.
[3] K. M. Almatar, “Smart transportation planning and its challenges in the Kingdom of Saudi Arabia,” Sustainable Futures, vol. 8,
no. December 2023, 2024, doi: 10.1016/j.sftr.2024.100238.
[4] S. Khan, S. Khan, A. Sulaiman, M. S. Al Reshan, H. Alshahrani, and A. Shaikh, “Deep neural network and trust management
approach to secure smart transportation data in sustainable smart cities,” ICT Express, vol. 10, no. 5, pp. 1059–1065, 2024,
doi: 10.1016/j.icte.2024.08.006.
[5] K. Jalil, Y. Xia, Q. Chen, M. N. Zahid, T. Manzoor, and J. Zhao, “Integrative review of data sciences for driving smart mobility in
intelligent transportation systems,” Computers and Electrical Engineering, vol. 119, 2024, doi: 10.1016/j.compeleceng.2024.109624.
[6] E. Dilek and M. Dener, “Computer vision applications in intelligent transportation systems: a survey,” Sensors, vol. 23, no. 6,
2023, doi: 10.3390/s23062938.
[7] H. Chi, Y. Lu, C. Xie, W. Ke, and B. Chen, “Spatio-temporal attention based collaborative local–global learning for traffic flow
prediction,” Engineering Applications of Artificial Intelligence, vol. 139, 2025, doi: 10.1016/j.engappai.2024.109575.
[8] H. Xing, A. Chen, and X. Zhang, “RL-GCN: traffic flow prediction based on graph convolution and reinforcement learning for
smart cities,” Displays, vol. 80, 2023, doi: 10.1016/j.displa.2023.102513.
[9] C. Ma, Y. Hu, and X. Xu, “Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow
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Transportation Engineering, vol. 18, 2024, doi: 10.1016/j.treng.2024.100279.
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trajectory generation and surrogate traffic safety indicators,” Accident Analysis and Prevention, vol. 191, 2023, doi:
10.1016/j.aap.2023.107191.
[12] H. Gao et al., “A hybrid deep learning model for urban expressway lane-level mixed traffic flow prediction,” Engineering
Applications of Artificial Intelligence, vol. 133, 2024, doi: 10.1016/j.engappai.2024.108242.
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[15] H. A. Saputri, M. Avrillio, L. Christofer, V. Simanjaya, and I. N. Alam, “Implementation of YOLO v7 algorithm in estimating
traffic flow in Malang,” Procedia Computer Science, vol. 245, pp. 117–126, 2024, doi: 10.1016/j.procs.2024.10.235.
[16] J. Tang, C. Ye, X. Zhou, and L. Xu, “YOLO-fusion and internet of things: advancing object detection in smart transportation,”
Alexandria Engineering Journal, vol. 107, pp. 1–12, 2024, doi: 10.1016/j.aej.2024.09.012.
[17] R. Ronariv, R. Antonio, S. F. Jorgensen, S. Achmad, and R. Sutoyo, “Object detection algorithms for car tracking with euclidean
distance tracking and YOLO,” Procedia Computer Science, vol. 245, pp. 627–636, 2024, doi: 10.1016/j.procs.2024.10.289.
[18] X. Zhai, Z. Huang, T. Li, H. Liu, and S. Wang, “YOLO-drone: an optimized YOLOv8 network for tiny UAV object detection,”
Electronics, vol. 12, no. 17, 2023, doi: 10.3390/electronics12173664.
[19] I. Shamta, F. Demir, and B. E. Demir, “Predictive fault detection and resolution using YOLOv8 segmentation model: a
comprehensive study on hotspot faults and generalization challenges in computer vision,” Ain Shams Engineering Journal,
vol. 15, no. 12, 2024, doi: 10.1016/j.asej.2024.103148.
[20] K. Wang, C. Ma, Y. Qiao, X. Lu, W. Hao, and S. Dong, “A hybrid deep learning model with 1DCNN-LSTM-attention networks
for short-term traffic flow prediction,” Physica A: Statistical Mechanics and its Applications, vol. 583, 2021,
doi: 10.1016/j.physa.2021.126293.
[21] J. Lu, “An efficient and intelligent traffic flow prediction method based on LSTM and variational modal decomposition,”
Measurement: Sensors, vol. 28, 2023, doi: 10.1016/j.measen.2023.100843.
[22] B. Naheliya, P. Redhu, and K. Kumar, “MFOA-Bi-LSTM: an optimized bidirectional long short-term memory model for short-term
traffic flow prediction,” Physica A: Statistical Mechanics and its Applications, vol. 634, 2024, doi: 10.1016/j.physa.2023.129448.
[23] J. D. Wang and C. O. N. Susanto, “Traffic flow prediction with heterogenous data using a hybrid CNN-LSTM model,”
Computers, Materials and Continua, vol. 76, no. 3, pp. 3097–3112, 2023, doi: 10.32604/cmc.2023.040914.
[24] A. R. Sattarzadeh, R. J. Kutadinata, P. N. Pathirana, and V. T. Huynh, “A novel hybrid deep learning model with ARIMA conv-
LSTM networks and shuffle attention layer for short-term traffic flow prediction,” Transportmetrica A: Transport Science,
vol. 21, no. 1, pp. 388–410, 2025, doi: 10.1080/23249935.2023.2236724.
[25] Y. Luo, J. Zheng, X. Wang, Y. Tao, and X. Jiang, “GT-LSTM: a spatio-temporal ensemble network for traffic flow prediction,”
Neural Networks, vol. 171, pp. 251–262, 2024, doi: 10.1016/j.neunet.2023.12.016.
[26] K. Zhao, D. Guo, M. Sun, C. Zhao, and H. Shuai, “Short-term traffic flow prediction based on VMD and IDBO-LSTM,” IEEE
Access, vol. 11, pp. 97072–97088, 2023, doi: 10.1109/ACCESS.2023.3312711.
[27] Bharti, P. Redhu, and K. Kumar, “Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-
LSTM,” Physica A: Statistical Mechanics and its Applications, vol. 625, 2023, doi: 10.1016/j.physa.2023.129001.
[28] C. Chaoura, H. Lazar, and Z. Jarir, “Traffic flow prediction at intersections: enhancing with a Hybrid LSTM-PSO approach,”
International Journal of Advanced Computer Science and Applications, vol. 15, no. 5, pp. 494–501, 2024,
doi: 10.14569/IJACSA.2024.0150549.
[29] S. Suyanto, A. A. Ariyanto, and A. F. Ariyanto, “Komodo Mlipir algorithm,” Applied Soft Computing, vol. 114, 2022,
doi: 10.1016/j.asoc.2021.108043.
[30] G. K. Jati, G. Kuwanto, T. Hashmi, and H. Widjaja, “Discrete komodo algorithm for traveling salesman problem,” Applied Soft
Computing, vol. 139, May 2023, doi: 10.1016/j.asoc.2023.110219.
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BIOGRAPHIES OF AUTHORS
Imam Ahmad Ashari received his bachelor's degree in Informatics from
Universitas Negeri Semarang in 2016 and his master's degree in Information Systems from
Universitas Diponegoro in 2019. He is currently a lecturer at the Informatics Study Program,
Universitas Harapan Bangsa. His research interests include the internet of things (IoT),
computer vision, and artificial intelligence. He can be contacted at email:
[email protected].
Wahyul Amien Syafei received a bachelor's degree from Diponegoro University,
Indonesia in 1995, a master's degree from Sepuluh Nopember Institute of Technology,
Indonesia in 2002, and a doctoral degree from Kyushu Institute of Technology, Japan in 2010.
Currently, he is an associate professor at the Master Program of Electrical Engineering,
Faculty of Engineering, Diponegoro University. He is also a lecturer at the Master Program of
Electrical Engineering, Postgraduate School, Diponegoro University. His research interests
include transmission channels, digital communication systems, multimedia, multimedia
networks, computer graphics, data communication, wireless and mobile networks, multimedia
telecommunication, information systems, and signal transformation processing. He can be
contacted at email: [email protected].
Adi Wibowo received the B.Sc. degree in Mathematics from Universitas
Diponegoro, Indonesia, in 2005, the M.Sc. degree in Computer Science from Universitas
Indonesia in 2011, and the Ph.D. degree in Engineering from Nagoya University, Japan, in
2016. He has been an Assistant Professor with the Department of Informatics, Universitas
Diponegoro, Indonesia, since 2006. His research interests primarily focus on artificial
intelligence, deep learning, computer vision, bioinformatics, and data mining. He has authored
or coauthored several publications in these areas. He can be contacted at email:
[email protected].
Traffic flow prediction using long short-term memory-Komodo Mlipir Algorithm: … (Imam Ahmad Ashari)
3353
[33] N. E. Khalifa, M. Loey, and S. Mirjalili, “A comprehensive survey of recent trends in deep learning for digital images
augmentation,” Artificial Intelligence Review, vol. 55, no. 3, pp. 2351–2377, 2022, doi: 10.1007/s10462-021-10066-4.
[34] B. A. Awaluddin, C. T. Chao, and J. S. Chiou, “Investigating effective geometric transformation for image augmentation to
improve static hand gestures with a pre-trained convolutional neural network,” Mathematics, vol. 11, no. 23, 2023,
doi: 10.3390/math11234783.
[35] M. Firdaus, K. Kusrini, and M. R. Arief, “Impact of data augmentation techniques on the implementation of a combination model
of convolutional neural network (CNN) and multilayer perceptron (MLP) for the detection of diseases in rice plants,” Journal of
Scientific Research, Education, and Technology (JSRET), vol. 2, no. 2, pp. 453–465, 2023, doi: 10.58526/jsret.v2i2.94.
[36] M. M. Rafi et al., “Performance analysis of deep learning YOLO models for South Asian Regional vehicle recognition,”
International Journal of Advanced Computer Science and Applications, vol. 13, no. 9, pp. 864–873, 2022, doi:
10.14569/IJACSA.2022.01309100.
[37] A. Betti and M. Tucci, “YOLO-S: A lightweight and accurate YOLO-like network for small target selection in aerial imagery,”
Sensors, vol. 23, no. 4, pp. 1–22, 2023, doi: 10.3390/s23041865.
[38] S. Hamiane, Y. Ghanou, H. Khalifi, and M. Telmem, “Comparative analysis of LSTM, ARIMA, and hybrid models for
forecasting future GDP,” Ingenierie des Systemes d’Information, vol. 29, no. 3, pp. 853–861, 2024, doi: 10.18280/isi.290306.
[39] C. L. Yang, A. A. Yilma, H. Sutrisno, B. H. Woldegiorgis, and T. P. Q. Nguyen, “LSTM-based framework with metaheuristic
optimizer for manufacturing process monitoring,” Alexandria Engineering Journal, vol. 83, pp. 43–52, 2023, doi:
10.1016/j.aej.2023.10.006.
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BIOGRAPHIES OF AUTHORS
Imam Ahmad Ashari received his bachelor's degree in Informatics from
Universitas Negeri Semarang in 2016 and his master's degree in Information Systems from
Universitas Diponegoro in 2019. He is currently a lecturer at the Informatics Study Program,
Universitas Harapan Bangsa. His research interests include the internet of things (IoT),
computer vision, and artificial intelligence. He can be contacted at email:
[email protected].
Wahyul Amien Syafei received a bachelor's degree from Diponegoro University,
Indonesia in 1995, a master's degree from Sepuluh Nopember Institute of Technology,
Indonesia in 2002, and a doctoral degree from Kyushu Institute of Technology, Japan in 2010.
Currently, he is an associate professor at the Master Program of Electrical Engineering,
Faculty of Engineering, Diponegoro University. He is also a lecturer at the Master Program of
Electrical Engineering, Postgraduate School, Diponegoro University. His research interests
include transmission channels, digital communication systems, multimedia, multimedia
networks, computer graphics, data communication, wireless and mobile networks, multimedia
telecommunication, information systems, and signal transformation processing. He can be
contacted at email: [email protected].
Adi Wibowo received the B.Sc. degree in Mathematics from Universitas
Diponegoro, Indonesia, in 2005, the M.Sc. degree in Computer Science from Universitas
Indonesia in 2011, and the Ph.D. degree in Engineering from Nagoya University, Japan, in
2016. He has been an Assistant Professor with the Department of Informatics, Universitas
Diponegoro, Indonesia, since 2006. His research interests primarily focus on artificial
intelligence, deep learning, computer vision, bioinformatics, and data mining. He has authored
or coauthored several publications in these areas. He can be contacted at email:
[email protected].
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